Technology for analyzing sensor data to detect configurations of vehicle operation

ABSTRACT

Systems and methods for using collecting and analyzing device sensor data to determine whether an individual is an operator or a passenger of a vehicle are disclosed. According to certain aspects, an electronic device associated with the individual may collect or access sensor data that is indicative of or associated with an operation of the vehicle. The electronic device may transmit pertinent portion(s) of the sensor data to a backend server, which may input the portion(s) into a neural network for analysis. The neural network may output a probability metric(s) indicative of whether the individual is a passenger or an operator of the vehicle.

FIELD

The present disclosure is directed to technologies for analyzing dataassociated with use of a vehicle. In particular, the present disclosureis directed to systems and methods for analyzing device sensor data todetect or estimate whether certain individuals are passengers oroperators of vehicles.

BACKGROUND

As mobile electronic devices become more ubiquitous, the amount of datagenerated by sensors of the electronic device is increasing. Forexample, most mobile devices include an accelerometer, a locationmodule, a gyroscope, and other sensors. Entities such as companies maycollect certain sensor data and use the data for various analyses orapplications. For example, a transportation fleet manager may collectlocation data to manage the locations of vehicles in its fleet.

Entities that issue policies or accounts to individuals may also usecertain sensor data to manage the terms of the policies or accounts. Forexample, an insurance provider may issue vehicle insurance policies toindividuals, and may measure the quality of vehicle operation usingcollected sensor data and process the policies accordingly (e.g.,processing “good driver” discounts resulting from safe vehicleoperation). However, it is not always the case that an individual, whentraveling in a vehicle, is the operator of the vehicle, yet dataassociated with the vehicle operation may be reflected as if theindividual operated the vehicle. These instances result in inaccuratepolicy processing.

Accordingly, there is an opportunity for data collection and analysistechniques to determine whether individuals are operators or passengersof vehicles.

SUMMARY

In an embodiment, a computer-implemented method of detecting aconfiguration of a vehicle is provided. The method may includereceiving, via a network connection, a set of sensor data from anelectronic device associated with an individual, the set of sensor data(i) indicating a set of timestamps corresponding to when the set ofsensor data was recorded, and (ii) including at least a set ofacceleration data; analyzing, by a computer processor, at least aportion of the set of sensor data using a neural network, the neuralnetwork previously trained using a set of training data, includingcalculating, based on at least the portion of the set of sensor data, atleast one probability indicative of whether the individual is anoperator or a passenger of the vehicle; and deeming, based on the atleast one probability, that the individual is either the operator or thepassenger of the vehicle.

In another embodiment, a system for of detecting a configuration of avehicle is provided. The system may include a communication moduleconfigured to communicate with an electronic device associated with anindividual via at least one network connection, a memory storing aneural network previously trained using a set of training data, and aset of computer-executable instructions, and a processor interfacingwith the communication module and the memory. The processor may beconfigured to execute the computer-executable instructions to cause theprocessor to receive, from the electronic device via the communicationmodule, a set of sensor data (i) indicating a set of timestampscorresponding to when the set of sensor data was recorded, and (ii)including at least a set of acceleration data, analyze at least aportion of the set of sensor data using the neural network, the neuralnetwork calculating, based on at least the portion of the set of sensordata, at least one probability indicative of whether the individual isan operator or a passenger of the vehicle, and deem, based on the atleast one probability, that the individual is either the operator or thepassenger of the vehicle.

In another embodiment, a computer-implemented method in an electronicdevice associated with an individual is provided. The method may includeaccessing a set of sensor data recorded by a set of sensors incorporatedin the electronic device, the set of sensor data including at least aset of acceleration data and a set of location data; determining, by acomputer processor based on the set of sensor data, at least a portionof the set of sensor data that is indicative of a set of movements ofthe electronic device in association with a vehicle; and transmitting atleast the portion of the set of sensor data to a server via a networkconnection, wherein the server analyzes at least the portion of the setof sensor data using a neural network to calculate at least oneprobability indicative of whether the individual is an operator or apassenger of the vehicle.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an overview of components and entities associated withthe systems and methods, in accordance with some embodiments.

FIG. 2 depicts an example signal diagram associated with collecting andanalyzing sensor data associated with use of a vehicle, in accordancewith some embodiments.

FIGS. 3A and 3B depict example user interfaces associated withprocessing an account or policy of an individual, in accordance withsome embodiments.

FIG. 4 depicts an example flow diagram associated with detecting aconfiguration of a vehicle, in accordance with some embodiments.

FIG. 5 depicts another example flow diagram associated with detecting aconfiguration of a vehicle, in accordance with some embodiments.

FIG. 6 is a hardware diagram of an example electronic device and anexample server, in accordance with some embodiments.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, collecting andanalyzing various combinations of sensor data and other informationrelated to use or operation of a vehicle to determine or estimatewhether a designated individual is an operator or a passenger of thevehicle. According to certain aspects, an electronic device may beassociated with the designated individual, where the electronic devicemay be equipped with a set of sensors. The electronic device may collectsensor data associated with or in temporal proximity to use or operationof the vehicle and transmit at least a portion of the sensor data to abackend server that maintains a trained neural network. The backendserver uses the neural network to analyze the portion of the sensor dataand calculate at least one probability indicative of whether thedesignated individual is an operator or a passenger of the vehicle.

The systems and methods therefore offer numerous benefits. Inparticular, the systems and methods facilitate identification andanalysis of pertinent sensor data gathered from personal electronicdevices using neural networks, which results in efficient and effectivedata analysis. The neural network may output probabilities thataccurately reflect whether individuals are operators or passengers ofvehicles. Thus, an entity may process policies or accounts ofindividuals to more accurately reflect these configurations. Notably,the entity may issue or modify policies or accounts of individuals basedon an analysis of sensor data generated by personal electronic devices,thus negating the need to communicate with or install hardware invehicles. Additionally, individuals may be properly awarded withbenefits or incentives that result from vehicle operation that iscorrectly attributed to them. It should be appreciated that additionalbenefits are envisioned.

The systems and methods discussed herein address a challenge that isparticular to policy or account management. In particular, the challengerelates to a difficulty in effectively and efficiently identifying andanalyzing sensor data that is pertinent to the configuration of vehicleoperation. Conventionally, entities process an individual'svehicle-related policy using collected data, regardless of whether theindividual actually operated the vehicle in the instance that resultedin the collected data. Thus, the individual's vehicle-related policy maybe inaccurately processed. The systems and methods offer improvedcapabilities to solve these problems by identifying portions of sensordata that are pertinent to the individual's role (i.e., operator orpassenger) in the vehicle, and analyzing the portions of sensor datausing a trained neural network. Further, because the systems and methodsemploy the capture, analysis, and transmission of data between and amongmultiple devices, the systems and methods are necessarily rooted incomputer technology in order to overcome the noted shortcomings thatspecifically arise in the realm of policy or account management.Additionally, the systems and methods enable more accurate processing ofpolicies tied specifically to individuals.

The use of one or more neural networks to analyze data is describedthroughout this specification. However, it should be appreciated thatadditional or alternative machine learning systems and techniques may beemployed. For example, the systems and methods may support decision treelearning, association rule learning, deep learning, inductive logicprogramming, support vector machines, clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, genetic algorithms, rule-basedmachine learning, learning classifier systems, and/or others.

FIG. 1 illustrates an overview of a system 100 of components configuredto facilitate the systems and methods. It should be appreciated that thesystem 100 is merely an example and that alternative or additionalcomponents are envisioned.

As illustrated in FIG. 1, the system 100 may include a vehicle 106 whichmay be, for example, an automobile, car, truck, motorcycle, motorbike,scooter, boat, recreational vehicle, or any other type of vehiclecapable of being operated or driven by an operator. The vehicle 106 maybe operated manually by a vehicle operator and/or may operateautonomously by a computer via the collection and analysis of varioussensor data. Although FIG. 1 depicts the single vehicle 106, it shouldbe appreciated that additional vehicles are envisioned. The vehicle 106may include an electronic device 103, which may be an on-board system,device, or component installed within the vehicle 106, an on-boarddiagnostic (OBD) system or any other type of system configured to beinstalled in the vehicle 106, such as an original equipment manufacturer(OEM) system.

The system 100 may further include an electronic device 105, which maybe any type of electronic device such as a mobile device (e.g., asmartphone), notebook computer, tablet, phablet, GPS (Global PositioningSystem) or GPS-enabled device, smart watch, smart glasses, smartbracelet, wearable electronic, PDA (personal digital assistants), pager,computing device configured for wireless communication, and/or the like.The electronic device 105 may be equipped or configured with a set ofsensors, such as a location module (e.g., a GPS chip), an image sensor,an accelerometer, a clock, a gyroscope, a compass, a yaw rate sensor, atilt sensor, and/or other sensors.

The electronic device 105 may belong to or be otherwise associated withan individual 108, where the individual 108 may be an owner of thevehicle 106 or otherwise associated with the vehicle 106. For example,the individual 108 may rent the vehicle 106 for a variable or allottedtime period, or the individual 108 may operate (or be a passenger of)the vehicle 106 as part of a ride share. Generally, the individual 108may operate the vehicle 106 (and may thus be an operator of thevehicle), or may be a passenger of the vehicle 106 (e.g., if anotherindividual is driving the vehicle 106 or the vehicle 106 is operatingautonomously). According to embodiments, the individual 108 may carry orotherwise have possession of the electronic device 105 during operationof the vehicle 106, regardless of whether the individual 108 is theoperator or passenger of the vehicle 106.

The system 100 may further include a processing server 110 connected toor configured to maintain a database 112 capable of storing variousdata. According to embodiments, the processing server 110 may beassociated with an entity such as a company, enterprise, business,individual, or the like, and may issue or maintain policies or accountsfor individuals or customers. For example, the processing server 110 mayissue a vehicle insurance policy for the individual 108, regardless ofwhich vehicle the individual 108 operates or travels in, where the termsand rates for the vehicle insurance policy may be based on theperformance and/or amount of vehicle operation undertaken by theindividual 108, and/or on other factors. For example, the vehicleinsurance policy may include a “good driver discount” that offers apremium discount for safe driving. As another example, the processingserver 110 may issue a vehicle insurance policy that is particular tothe vehicle 106, independent of who is operating or traveling in thevehicle 106.

The electronic device 105 may communicate with the processing server 110via one or more networks 115. In embodiments, the network(s) 115 maysupport any type of data communication via any standard or technology(e.g., GSM, CDMA, TDMA, WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB,Internet, IEEE 802 including Ethernet, WiMAX, Wi-Fi, Bluetooth, andothers).

According to embodiments, the processing server 110 may configure ortrain one or more neural networks, the data of which may be stored inthe database 112. Generally, neural networks (sometimes calledartificial neural networks (ANNs)) are used in various applications toestimate or approximate functions dependent on a set of inputs. Forexample, neural networks may be used in speech recognition and toanalyze images and video. Generally, neural networks are composed of aset of interconnected processing elements or nodes which processinformation by its dynamic state response to external inputs. Eachneural network may consist of an input layer, one or more hidden layers,and an output layer. The one or more hidden layers are made up ofinterconnected nodes that process input via a system of weightedconnections. Some neural networks are capable of updating by modifyingtheir weights according to their training outputs, while other neuralnetworks are “feedfoward” in which the information does not form acycle. There are many types of neural networks, where each neuralnetwork may be tailored to a different application, such as dataanalysis, computer vision, speech recognition, image analysis, andothers.

According to embodiments, the neural network employed by the systems andmethods may analyze sensor data and output a set of probabilitiesindicative of whether the individual 108 is an operator or a passengerof the vehicle 106. The processing server 110 may initially train theneural network using a set of training data including, as inputs,various types of sensor data. In particular, the set of training datamay include location data, acceleration data, timing data, and/or othertypes of data, and may be sourced from simulated or collected data. Theset of training data may also include designated outputs correspondingto the inputs, where the outputs may be percentages or probabilitiesindicative of whether individuals are operators or passengers ofvehicles. Although the embodiments discussed herein describe the neuralnetwork data as being stored on the database 112, it should beappreciated that the electronic device 105 may alternatively oradditionally store the neural network data. In this regard, theelectronic device 105 may locally facilitate analysis of captured sensordata using the neural network(s).

In an implementation, an output of the neural network may include afirst probability representative of a given individual being theoperator and a second probability representative of the given individualbeing the passenger. For example, the set of training data may includeacceleration data consistent with an individual entering a vehicle fromthe left side, and an output(s) that includes a first probability of 55%and a second probability of 45%. The set of training data may indicatecombinations of the inputs along with respective mappings to theoutputs, as understood in the art. It should be appreciated that theprocessing server 110 may train the neural network according to variousconventions or techniques, with varying amounts and sizes of trainingdata. After the processing server 110 trains the neural network, thedatabase 112 may store data associated with the trained neural network.

In operation, the electronic device 105 may receive, collect, request,or otherwise access sensor data from the set of sensors incorporatedtherein, where the electronic device 105 may access the sensor databefore, during, or after operation of the vehicle 106 (i.e., when theengine of the vehicle 106 is on). The electronic device 105 may identifya portion(s) of the sensor data that may be indicative of whether theindividual 108 is the operator of a passenger of the vehicle 106.

In an implementation, the electronic device 105 may alternatively oradditionally interface with the electronic device 103 and may retrievesensor data or other information gathered by the electronic device 103.The sensor data retrieved from the electronic device 103 may be vehiclesensor data generated by vehicle sensors such as, for example, alocation module (e.g., a global positioning system (GPS) chip), an imagesensor, an accelerometer, an ignition sensor, a clock, a speedometer, atorque sensor, a gyroscope, a throttle position sensor, a compass, a yawrate sensor, a tilt sensor, a steering angle sensor, a brake sensor,and/or other sensors. The retrieved sensor data may supplement thesensor data generated by the sensors of the electronic device 105.According to embodiments, the electronic device 105 may collect itsdevice sensor data continuously, and/or when retrieval of vehicle sensordata is not available.

The electronic device 105 may transmit the sensor data, or portion(s) ofthe sensor data, to the processing server 110 via the network(s) 115.The processing server 110 may input the sensor data or portion(s) of thesensor data into the neural network, and the neural network may output aprobability metric(s) indicative of whether the individual is theoperator or a passenger of the vehicle 106. The processing server 110may process a policy or account of the individual 108 based at least inpart on the outputted probability metric(s). Additional details anddescriptions of these and other functionalities are described withrespect to FIG. 2.

FIG. 2 depicts a signal diagram 200 associated with collecting andanalyzing sensor data associated with use of a vehicle. The signaldiagram 200 includes a set of sensors 207 (such as any of the sensors ofthe electronic device 105 as discussed with respect to FIG. 1), aprocessor 206, and a server 210 (such as the processing server 110 asdiscussed with respect to FIG. 1). In an implementation, the set ofsensors 207 and the processor 206 may be incorporated into an electronicdevice 205 (such as the electronic device 105 as discussed with respectto FIG. 1). The server 210 may be located remotely from the processor206 and the set of sensors 207, where the electronic device 205 maycommunicate with the server 210 via one or more network connections. Theprocessor 206 may execute a dedicated application configured tofacilitate the described functionalities.

The signal diagram 200 may begin when the server 210 receives (222)training data and a set of corresponding training labels as an input.According to embodiments, the training data may include various types ofdata including acceleration data, location data, timing data, and maygenerally include simulated data (e.g., gathered in a virtualenvironment having a physics engine) or real-world data collected fromdevices. The training data may also include labels (e.g., “passenger”and “driver”) to be used in the training. The server 210 may train (224)at least one neural network with the training data and correspondinglabels using various backpropagation or other training techniques. Inparticular, the server 210 may train the at least one neural network byanalyzing the inputted data and arriving at outputs(s). By recursivelyarriving at outputs, comparing the outputs to the training labels, andminimizing the error between the outputs and the training labels, thecorresponding neural network(s) may train itself according to the inputparameters. According to embodiments, the trained neural network(s) maybe configured with a set of corresponding edge weights which enable thetrained neural network(s) to analyze new inputted data. The server 210may locally store, or otherwise be configured to access, the trainedneural network(s).

In an optional embodiment, and independent from the neural networktraining, the processor 206 may request (226) sensor data from the setof sensors 207. According to embodiments, the processor 206 mayautomatically request the sensor data periodically (e.g., once every tenseconds, once every minute, once every hour), or a user of theelectronic device 205 may cause the processor 206 to request the sensordata. Additionally, the processor 206 may request specific sensor data,such as sensor data recorded within a specified timeframe, sensor dataof a specific location or area, and/or sensor data according to otherparameters.

The set of sensors 207 may generate (228), sense, or otherwise collect aset of sensor data. According to embodiments, the set of sensor data mayinclude at least acceleration data (e.g., measured in g-force) andlocation data (e.g., GPS coordinates). Additionally or alternatively,the sensor data may indicate connection data representing any connectioninstances to additional sensors or components associated with thevehicle. For example, the sensor data may indicate that the electronicdevice 205 paired to the vehicle via a personal area network (PAN)connection.

In embodiments, the set of sensors 207 may generate the set of sensordata continuously, over a specified period of time, and/or in accordancewith any request received from the processor 206. The set of sensors 207may provide (230) the set of sensor data to the processor 206. Inembodiments, the set of sensors 207 may provide the set of sensor datacontinuously as the sensor data is generated, or at a specified time(s).

The processor 206 may reconcile (232) clock or timing data that may begenerated by a clock or other timing mechanism of the electronic device205. In embodiments, the set of sensor data may include timestampscorresponding to when the respective readings in the set of sensor datawere recorded. For example, a certain accelerometer reading and acertain location reading may each include a corresponding timestamp. Inan implementation, the processor 206 may associate the set of sensordata with a timestamp generated by a clock upon the processor 206receiving the set of sensor data from the set of sensors 207.

According to embodiments, the processor 206 may identify a portion(s) ofthe data that may be relevant to use of the vehicle. In a particularinstance, the processor 206 may determine that the location dataindicates that the electronic device 205 is located in proximity to adesignated vehicle location (e.g., a garage, a driveway, etc.), and mayidentify a portion of the sensor data that corresponds to the respectivelocation data (e.g., acceleration data recorded at or near the time thatthe location data was recorded). In another instance, the processor 206may determine from the set of sensor data that the electronic device 205is traveling above a threshold speed, which may indicate that theelectronic device 205 is located within a vehicle, and may identify aportion of the set of sensor data that has a temporal proximity (e.g.,before, during, and/or after) to the assessed sensor data.

In a further instance, the processor 206 may identify a portion of theset of sensor data that is recorded before, during, or after theelectronic device 205 connects to a device associated with the vehicle.For example, the electronic device 205 may pair with (or disconnectfrom) the vehicle or may detect that the vehicle is started (orstopped), in which case a relevant portion of the set of sensor data(e.g., location data, accelerometer readings, etc.) may be the sensordata having temporal proximity to the electronic device 205 pairing with(or disconnecting from) the vehicle or detecting the vehicle is started(or stopped). It should be appreciated that the processor 206 mayidentify, in the sensor data, additional instances that may berepresentative of usage or operation of the vehicle.

The processor 206 may cause (234) the set of sensor data and the timingdata to be transmitted to the server 210 via one or more networkconnections. In particular, the processor 206 may cause any identifiedrelevant portions of the set of sensor data and the corresponding timingdata to be transmitted to the server 210. It should be appreciated thatthe processor 206 may transmit the data continuously as the processor206 receives and/or analyzes the data, periodically (e.g., once everyten seconds, minute, hour, etc.), or in response to a certain trigger(e.g., a connection being established between the electronic device 205and the server 210, a request from a user of the electronic device 205,etc.). It should further be appreciated that the processor 206 maytransmit the sensor data to the server 210, at which point the server210 may alternatively or additionally examine the sensor data and thetiming data to identify relevant portions of the data, as discussedabove.

After receiving the data, the server 210 may analyze (236) the sensorand timing data using the neural network that was trained in (224). Inoperation, the server 210 may input a portion of the sensor and timingdata into the neural network, where the neural network may provide anassociated output(s) that may represent a probability(ies) (e.g., on ascale of 0%-100%, or other ranges) of whether the individual associatedwith the received data is a passenger of the vehicle or an operator ofthe vehicle. In an embodiment, the server 210 may at a minimum consideracceleration data and location data. However, it should be appreciatedthat the server 210 may analyze any combination of data types asinput(s).

In an example use case, the inputted data may include acceleration datarecorded in proximity to an individual entering or exiting a vehicle,where the acceleration data may be indicative of whether the individualentered or exited the vehicle from the right side or the left side ofthe vehicle. If the individual entered or exited the vehicle from theright side, then there is virtual certainty that the individual is notthe operator of the vehicle, and if the individual entered or exited thevehicle from the left side, then there is about a 50% chance (orgreater) that the individual is the operator of the vehicle.Accordingly, the neural network may receive the acceleration data as aninput, and may output a probability that the individual entered from theright side, a probability that the individual entered from the leftside, and a probability of whether the individual is the operator or thepassenger.

Additionally or alternatively, the timing data may indicate the timebetween an individual entering a vehicle and the vehicle being startedor starting movement, where a shorter time may increase the probabilitythat the individual is a passenger. Similarly, the timing data mayindicate the time between the vehicle being stopped or stopping movementand the individual exiting the vehicle, where a shorter time mayincrease the probability that the individual is a passenger. This may beespecially evident in situations in which the individual is a passengerin a ride share vehicle, as the individual often enters (or exits) thevehicle immediately before (or after) the vehicle departs from (orarrives at) its origin (or destination). The neural network may accountfor this relevant timing data, and the output(s) may reflect as such.

In another example use case, the location data may indicate a known ordesignated location for a vehicle, such as a garage, driveway, or otherlocation where the vehicle may commonly be located. When the locationdata indicates such a known or designated location, the probability thatthe individual is the operator of the vehicle increases, which may bereflected in the output of the neural network.

In a further example use case, the set of sensor data may indicatemovement of the vehicle that is indicative of vehicle ownership, andtherefore that the probability that the individual is the operator ofthe vehicle may be increased. For example, the acceleration data and/orlocation data may indicate instances of the vehicle traveling in reverse(e.g., indicative of backing out of a driveway) and/or instances of thevehicle backing up and moving forward (e.g., indicative of backing outof a parking spot). The neural network may account for this relevantdata, and the output(s) may reflect as such.

In an additional example use case, the set of sensor data may indicatemovement of the electronic device 205 before, during, or after use ofthe vehicle. In particular, the set of sensor data may include instancesof use of the electronic device 205 during operation of the vehicle,which may increase the probability that the individual is a passenger ofthe vehicle. Further, the set of sensor data may indicate the electronicdevice's 205 placement into or retrieval from a stable location, such asa mount, a cup holder, or other place which may secure the electronicdevice 205 during operation of the vehicle, which may increase theprobability that the individual is the operator of the vehicle. Incontrast, the set of sensor data may indicate continued manual handlingof the electronic device 205, which may increase the probability thatthe individual is a passenger of the vehicle, as passengers tend tophysically hold electronic devices. The neural network may account forthis relevant data, as well as for timing data associated therewith, andthe output(s) may reflect as such.

In analyzing the sensor and timing data, the server 210 may determinethat additional data is needed to assess the configuration of thevehicle. In this instance, the server 210 may optionally request (238)additional data from the electronic device 205, in which case theelectronic device 205 and the set of sensors 207 may repeat (226) (228),(230), (232), and/or (234), either according to the request or operatingnormally.

Based on the analysis of the data, the server 210 may determine (240)the role or configuration of the user in association with the vehicle(i.e., whether the individual is likely to be the passenger or theoperator of the vehicle). As described herein, the neural network mayoutput one or more probabilities that may indicate a likelihood(s) ofwhether the individual is a passenger or an operator.

In embodiments, the neural network may include a set of layers that maynot be fully connected. The neural network may be trained tomeaningfully group together raw signal data (e.g., could be used topredict which side of the vehicle an individual exits from), where theunits of the neural network may be connected in one of the hiddenlayers. An advantage of this configuration is that the final error ofthe prediction may be back-propagate through the network, which mayresult in improved accuracy in role determinations.

In an implementation, the output(s) of the neural network may beprobability(ies) or percentage(s) associated with a certain feature(s)or scenario(s) indicative of roles of individuals. The output(s) may berepresentative of a softmax or normalized exponential functioncomprising a K-dimensional vector of real values in the range (0,1] thatadd up to one (1). In operation, a softmax function may receive, asinputs, a first initial probability representative of a first featureand a second initial probability representative of a second feature. Thesoftmax function may output a first final probability representative ofthe first feature and a second final probability representative of thesecond feature. In an alternative implementation, the neural network mayinclude an affine layer (i.e., a fully connected layer) that may take,as inputs, values from hidden units and matrix multiply the values bylearned weights, where a sigmoid function may process the output(s)therefrom. In operation, the affine layer may receive, as inputs, afirst probability representative of a first feature and a secondprobability representative of a second feature, where an output(s) ofthe affine layer may be processed by the sigmoid function.

It should be appreciated that the server 210 may consider combinationsof the probability(ies) as well as weightings for the probability(ies).In embodiments, the server 210 may also determine metrics associatedwith the operation of the vehicle, such as speed, location, drivingevents (e.g., hard braking, sudden acceleration, etc.), distancetraveled, operation time, and/or other data.

After determining the role or configuration, the server 210 may provide(242) an indication of the determined role or configuration to theelectronic device 205. The processor 206 may cause (246) the indicationof the determined role or configuration to be stored. Accordingly, theelectronic device 205 or the application executing thereon may maintaina record or log of instances when the individual is determined to be anoperator or passenger of a vehicle. In embodiments, the electronicdevice 205 may also be configured to display any metrics associated withoperation of the vehicle.

The server 210 may process (244) an account of the individual based onthe role or configuration determined in (240), any previously-determinedroles or configurations, as well as on any determined metrics associatedwith operation of the vehicle. In particular, the server 210 may adjustcertain terms, conditions, or rates of policies or accounts for theindividual. For example, if the server 210 determines that the qualityof operation of a vehicle is above average for the last five (5) timesan individual operated the vehicle, then the server 210 may reduce apremium amount of a vehicle insurance policy for the individual.Additionally or alternatively, the processor 206 may similarly process(248) the account of the individual. It should be appreciated that theserver 210 (or the processor 206) may process the account or policy inany manner according to any analysis of sensor data.

FIGS. 3A and 3B illustrate example interfaces associated with thesystems and methods. An electronic device, such as the electronic device205 as discussed with respect to FIG. 2, may be configured to displaythe interfaces and/or receive selections and inputs via the interfaces.One or more dedicated applications that are configured to operate on theelectronic device may display the interfaces, where an individual mayhave an account or policy that may be accessed by the application(s).

The electronic device may receive the content included in the interfacesfrom a server, such as the server 210 as discussed with respect to FIG.2. Additionally or alternatively, the electronic device may locallydetermine the content included in the interfaces, such as using sensordata collected during operation of a vehicle. It should be appreciatedthat the interfaces are merely examples and that alternative oradditional content is envisioned.

FIG. 3A illustrates an interface 350 depicting information associatedwith a recent vehicular operation, or trip, undertaken by theindividual. The interface 350 includes a set of information 351associated with the trip (as shown: a distance, duration, location, androle). The set of information 351 indicates that the individual wasdeemed the vehicle operator for the trip, such as via a neural networkanalysis of sensor data associated with the trip. The interface 350 mayinclude a correction selection 352 that enables the individual tocorrect the role determination. For example, if the individual wasactually a passenger in the vehicle on the trip, the individual mayselect the correction selection 352 to request an appropriate entity tocorrect the role designation. In embodiments, the server may store thecorrection along with the underlying sensor data, and may additionallytrain or update any associated neural network(s) using the correctionand any additional corrections, such that the server may use theretrained neural network in subsequent analyses.

The interface 350 may further include an information selection 353 that,upon selection, causes the electronic device to display informationassociated with an adjustment of a policy or account of the individual.Additionally, the interface may include an okay selection 354 that, uponselection, may cause the electronic device to dismiss the interface 350and proceed to other functionality.

FIG. 3B illustrates an interface 355 depicting information associatedwith an adjustment of a policy or account of the individual. Inembodiments, the electronic device may display the interface 355 inresponse to the individual selecting the information selection 353 ofthe interface 350 of FIG. 3A. The interface 335 includes a set ofinformation 356 related to the recent trip as well as adjustments madeto the policy or account of the individual. As shown in FIG. 3B, the setof information 356 indicates that the trip did not have any detectedabnormal driving events, and that the premium amount of the policy isbeing reduced by $1.00/month. The interface 355 includes an okayselection 357 that, upon selection, may cause the electronic device todismiss the interface 355 and proceed to other functionality.

FIG. 4 depicts is a block diagram of an example method 400 of detectinga configuration of a vehicle. The method 400 may be facilitated by aserver (such as the server 210 as discussed with respect to FIG. 2) thatmay communicate with one or more electronic devices or components. Itshould be appreciated that the server may manage or access a neuralnetwork previously trained with a set of training data.

The method 400 may begin when the server receives (block 405), via anetwork connection, a set of sensor data from an electronic deviceassociated with an individual. The set of sensor data may indicate a setof timestamps corresponding to when the set of sensor data was recorded,and include at least a set of acceleration data, however it should beappreciated that the set of sensor data may include alternative oradditional types of data.

The server may determine (block 410) at least a portion of the set ofsensor data that is indicative of a set of movements of the electronicdevice in association with a vehicle. In embodiments in which the set ofsensor data includes a set of location data, the server may determine,from at least a portion of the set of location data, that the electronicdevice is located in proximity to a designated vehicle location, andidentify at least a portion of the set of acceleration data thatcorresponds to at least the portion of the set of location data.Additionally or alternatively, the server may detect, from the set ofsensor data, an instance of the electronic device connecting to thevehicle, and identify at least a portion of the set of acceleration datathat is in temporal proximity to the instance of the electronic deviceconnecting to the vehicle.

The server may analyze (block 415) at least the portion of the set ofsensor data using a neural network. In particular, the server may input,into the neural network, at least the portion of the set of sensor data.The server may calculate (block 420), based on at least the portion ofthe set of sensor data, at least one probability indicative of whetherthe individual is an operator or a passenger of the vehicle. Inembodiments, the neural network may output, based on at least theportion of the set of sensor data, (i) a first probabilityrepresentative of the individual being the operator, and (ii) a secondprobability representative of the individual being the passenger.

The server may deem (block 425), based on the at least one probability,that the individual is either the operator or the passenger of thevehicle. In embodiments, the at least one probability may be associatedwith a certain feature or scenario, where the server may process the atleast one probability using a softmax function, an affine layer, and/ora sigmoid function, as discussed herein. The server may also compare theat least one probability to a respective threshold value(s). The servermay process (block 430) an account of the individual based on thedeeming. In particular, the server may modify any terms or conditions ofthe account based on whether the individual is deemed the operator orpassenger of the vehicle.

FIG. 5 depicts is a block diagram of an example method 500 of detectinga configuration of a vehicle. The method 500 may be facilitated by anelectronic device (such as the electronic device 205 as discussed withrespect to FIG. 2) that may communicate with one or more sensors,electronic devices, servers, or components.

The method 500 may begin when the electronic device accesses (block 505)a set of sensor data recorded by a set of sensors incorporated in theelectronic device. In embodiments, the set of sensor data may include atleast a set of acceleration data and a set of location data.

The electronic device may determine (block 510), based on the set ofsensor data, at least a portion of the set of sensor data that isindicative of a set of movements of the electronic device in associationwith a vehicle. In an embodiment, the electronic device may determine,based on the set of acceleration data and the set of location data, atleast the portion of the set of sensor data that is indicative ofmovement of the vehicle itself. Additionally or alternatively, theelectronic device may determine, from at least a portion of the set oflocation data, that the electronic device is located in proximity to adesignated vehicle location, and may identify at least a portion of theset of acceleration data that corresponds to at least the portion of theset of location data. Additionally or alternatively, the electronicdevice may detect, from the set of sensor data, an instance of theelectronic device connecting to the vehicle, and may identify at least aportion of the set of acceleration data that is in temporal proximity tothe instance of the electronic device connecting to the vehicle.

The electronic device may transmit (block 515) at least the portion ofthe set of sensor data to a server via a network connection, where theserver may analyze at least the portion of the set of sensor data usinga neural network. In particular, the neural network may receive at leastthe portion of the set of sensor data as inputs, and may output at leastone probability indicative of whether the individual is an operator or apassenger of the vehicle.

FIG. 6 illustrates a hardware diagram of an example electronic device605 (such as the electronic device 205 as discussed with respect to FIG.2) and an example server 610 (such as the server 210 as discussed withrespect to FIG. 2), in which the functionalities as discussed herein maybe implemented. It should be appreciated that the server 610 may beassociated with an entity that may issue and manage policies or accountsof individuals.

The electronic device 605 may include a processor 672 as well as amemory 678. The memory 678 may store an operating system 679 capable offacilitating the functionalities as discussed herein as well as a set ofapplications 675 (i.e., machine readable instructions). For example, oneof the set of applications 675 may be an analysis application 690configured to analyze sensor data and/or other information. It should beappreciated that one or more other applications 692 are envisioned, suchas an application associated with accessing a policy or account of anindividual.

The processor 672 may interface with the memory 678 to execute theoperating system 679 and the set of applications 675. According to someembodiments, the memory 678 may also include sensor data 680 includingdata accessed or collected from a set of sensors. The memory 678 mayinclude one or more forms of volatile and/or non-volatile, fixed and/orremovable memory, such as read-only memory (ROM), electronicprogrammable read-only memory (EPROM), random access memory (RAM),erasable electronic programmable read-only memory (EEPROM), and/or otherhard drives, flash memory, MicroSD cards, and others.

The electronic device 605 may further include a communication module 677configured to communicate data via one or more networks 612. Accordingto some embodiments, the communication module 677 may include one ormore transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 676. For example, the communication module 677 mayinterface with another device, component, or sensors via the network(s)612 to retrieve sensor data.

The electronic device 605 may include a set of sensors 671 such as, forexample, a location module (e.g., a GPS chip), an image sensor, anaccelerometer, a clock, a gyroscope, a compass, a yaw rate sensor, atilt sensor, and/or other sensors. The electronic device 605 may furtherinclude a user interface 681 configured to present information to a userand/or receive inputs from the user. As shown in FIG. 6, the userinterface 681 may include a display screen 682 and I/O components 683(e.g., ports, capacitive or resistive touch sensitive input panels,keys, buttons, lights, LEDs). According to some embodiments, the usermay access the electronic device 605 via the user interface 681 toreview information, make selections, and/or perform other functions.Additionally, the electronic device 605 may include a speaker 673configured to output audio data and a microphone 674 configured todetect audio.

In some embodiments, the electronic device 605 may perform thefunctionalities as discussed herein as part of a “cloud” network or mayotherwise communicate with other hardware or software components withinthe cloud to send, retrieve, or otherwise analyze data.

As illustrated in FIG. 6, the electronic device 605 may communicate andinterface with the server 610 via the network(s) 612. The server 610 mayinclude a processor 659 as well as a memory 656. The memory 656 maystore an operating system 657 capable of facilitating thefunctionalities as discussed herein as well as a set of applications 651(i.e., machine readable instructions). For example, one of the set ofapplications 651 may be an analysis application 652 configured toanalyze data using a set of neural networks. It should be appreciatedthat one or more other applications 653 are envisioned.

The processor 659 may interface with the memory 656 to execute theoperating system 657 and the set of applications 651. According to someembodiments, the memory 656 may also include neural network data 658,such as set(s) of training data and/or data associated with a trainedneural network(s). The memory 656 may include one or more forms ofvolatile and/or non-volatile, fixed and/or removable memory, such asread-only memory (ROM), electronic programmable read-only memory(EPROM), random access memory (RAM), erasable electronic programmableread-only memory (EEPROM), and/or other hard drives, flash memory,MicroSD cards, and others.

The server 610 may further include a communication module 655 configuredto communicate data via the one or more networks 612. According to someembodiments, the communication module 655 may include one or moretransceivers (e.g., WWAN, WLAN, and/or WPAN transceivers) functioning inaccordance with IEEE standards, 3GPP standards, or other standards, andconfigured to receive and transmit data via one or more external ports654. For example, the communication module 655 may receive, from theelectronic device 605, a set(s) of sensor data.

The server 610 may further include a user interface 662 configured topresent information to a user and/or receive inputs from the user. Asshown in FIG. 6, the user interface 662 may include a display screen 663and I/O components 664 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs). According to someembodiments, the user may access the server 610 via the user interface662 to review information, make changes, input training data, and/orperform other functions.

In some embodiments, the server 610 may perform the functionalities asdiscussed herein as part of a “cloud” network or may otherwisecommunicate with other hardware or software components within the cloudto send, retrieve, or otherwise analyze data.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code may be adapted to beexecuted by the processors 672, 659 (e.g., working in connection withthe respective operating systems 679, 657) to facilitate the functionsas described herein. In this regard, the program code may be implementedin any desired language, and may be implemented as machine code,assembly code, byte code, interpretable source code or the like (e.g.,via Golang, Python, Scala, C, C++, Java, Actionscript, Objective-C,Javascript, CSS, XML). In some embodiments, the computer program productmay be part of a cloud network of resources.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention may be defined by the words of the claims setforth at the end of this patent. The detailed description is to beconstrued as exemplary only and does not describe every possibleembodiment, as describing every possible embodiment would beimpractical, if not impossible. One could implement numerous alternateembodiments, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that may be permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that may betemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it may becommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment may be included in at leastone embodiment. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

As used herein, the terms “comprises,” “comprising,” “may include,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also may include the plural unless itis obvious that it is meant otherwise.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

What is claimed is:
 1. A computer-implemented method in a server fordetermining a role of an individual associated with a vehicle, themethod comprising: receiving, by the server, a set of sensor data froman electronic device of the individual associated with the vehicle, theset of sensor data including a set of timing data, a set of locationdata, and a set of acceleration data, the set of timing datacorresponding to when the set of location data and the set ofacceleration data are recorded, the set of location data beingindicative of the location of the electronic device, the set ofacceleration data being indicative of the side that the individualentered or exited the vehicle from; determining, by the server using atrained neural network, a probability of whether the individual being anoperator or a passenger of the vehicle based at least in part upon theset of timing data, the set of location data, and the set ofacceleration data; and determining, by the server, that the individualis either the operator or the passenger of the vehicle based upon thecalculated probability; wherein the determining a probability includes:determining, from the set of timing data based upon the set of locationdata, a time of interest that corresponds to when the electronic deviceis located in proximity to a designated vehicle location; identifying aportion of the set of acceleration data that corresponds to the time ofinterest; and inputting the portion of the set of acceleration data intothe trained neural network to determine the probability.
 2. Thecomputer-implemented method of claim 1, further comprising: processing,by the server, an account of the individual based upon determining thatthe individual is either the operator or the passenger of the vehicle.3. The computer-implemented method of claim 1, wherein the determining aprobability comprises inputting a first initial probabilityrepresentative of a first feature and a second initial probabilityrepresentative of a second feature into a softmax function, wherein anoutput of the softmax function includes a first final probabilityrepresentative of the first feature and a second final probabilityrepresentative of the second feature.
 4. The computer-implemented methodof claim 1, wherein the determining a probability comprises inputting afirst probability representative of a first feature and a secondprobability representative of a second feature into an affine layer ofthe neural network, and processing an output of the affine layer by asigmoid function.
 5. The computer-implemented method of claim 1, furthercomprising: determining, by the server, a portion of the set of sensordata that is indicative of a set of movements of the electronic devicein association with the vehicle.
 6. The computer-implemented method ofclaim 5, wherein determining the portion of the set of sensor data thatis indicative of the set of movements of the electronic device inassociation with the vehicle comprises: identifying a portion of the setof acceleration data that corresponds to a portion of the set oflocation data that corresponds to the designated vehicle location. 7.The computer-implemented method of claim 5, wherein determining theportion of the set of sensor data that is indicative of the set ofmovements of the electronic device in association with the vehiclefurther comprises: detecting an instance of the electronic deviceconnecting to the vehicle based upon the set of sensor data; andidentifying a portion of the set of acceleration data that is intemporal proximity to the instance of the electronic device connectingto the vehicle.
 8. The computer-implemented method of claim 1, whereindetermining the probability comprises inputting the set of timing data,the set of location data, and the set of acceleration data into thetrained neural network.
 9. A computing system for determining a role ofan individual associated with a vehicle, the computing systemcomprising: a processor; and a memory storing computer-executableinstructions, that when executed by the processor, cause the processorto: receive a set of sensor data from an electronic device of theindividual associated with the vehicle, the set of sensor data includinga set of timing data, a set of location data, and a set of accelerationdata, the set of timing data corresponding to when the set of locationdata and the set of acceleration data are recorded, the set of locationdata being indicative of the location of the electronic device, the setof acceleration data being indicative of at least one of the individualentering the vehicle and the individual exiting the vehicle; determine,using a trained neural network, a first probability of the individualbeing an operator of the vehicle based at least in part upon the set oftiming data, the set of location data, and the set of acceleration data;and determine that the individual is either the operator or thepassenger of the vehicle based upon the first probability; wherein todetermine a first probability includes to: determine, from the set oftiming data based upon the set of location data, a time of interest thatcorresponds to when the electronic device is located in proximity to adesignated vehicle location; identify a portion of the set ofacceleration data that corresponds to the time of interest; and inputthe portion of the set of acceleration data into the trained neuralnetwork to determine the first probability.
 10. The computing system ofclaim 9, wherein the computer-executable instructions, when executed bythe processor, further cause the processor to: process an account of theindividual based upon determining that the individual is either theoperator or the passenger of the vehicle.
 11. The computing system ofclaim 9, wherein to determine the first probability of the individualbeing the operator of the vehicle includes to determine a secondprobability representative of the individual being the passenger. 12.The computing system of claim 11, wherein to determine that theindividual is either the operator or the passenger of the vehicleincludes to determine that at least one of the first probability or thesecond probability meets a threshold value, and determine that theindividual is either the operator or the passenger of the vehicle basedupon at least one of the first probability or the second probabilitymeeting the threshold value.
 13. The computing system of claim 9,wherein the computer-executable instructions, when executed by theprocessor, further cause the processor to: determine a portion of theset of sensor data that is indicative of a set of movements of theelectronic device in association with the vehicle.
 14. The computingsystem of claim 13, wherein to determine the portion of the set ofsensor data that is indicative of the set of movements of the electronicdevice in association with the vehicle comprises to: identify a portionof the set of acceleration data that corresponds to a portion of the setof location data that corresponds to the designated vehicle location.15. The computing system of claim 13, wherein to determine the portionof the set of sensor data that is indicative of the set of movements ofthe electronic device in association with the vehicle comprises to:detect an instance of the electronic device connecting to the vehiclebased upon the set of sensor data; and identify a portion of the set ofacceleration data that is in temporal proximity to the instance of theelectronic device connecting to the vehicle.
 16. The computing system ofclaim 9, wherein to determine the first probability includes to inputthe set of timing data, the set of location data, and the set ofacceleration data into the trained neural network.
 17. Acomputer-implemented method for determining a role of an individualassociated with a vehicle, the method comprising: accessing sensor datarecorded by a set of sensors incorporated in an electronic device, thesensor data including timing data, location data, and acceleration data,the timing data corresponding to when the location data and theacceleration data are recorded, the location data being indicative ofthe location of the electronic device, the acceleration data beingindicative of the side that the individual entered or exited the vehiclefrom; and determining, using a trained neural network, a probability ofthe individual being an operator of the vehicle based at least in partupon the timing data, the location data, and acceleration data; whereinthe determining a probability includes: determining, from the timingdata based upon the location data, a time of interest that correspondsto when the electronic device is located in proximity to a designatedvehicle location; identifying a portion of the acceleration data thatcorresponds to the time of interest; and inputting the portion of theacceleration data into the trained neural network to determine theprobability.
 18. The computer-implemented method of claim 17, furthercomprising: determining a portion of the sensor data that is indicativeof a set of movements of the electronic device in association with thevehicle; and identifying a portion of the set of acceleration data thatcorresponds to the designated vehicle location.
 19. Thecomputer-implemented method of claim 17, further comprising: determininga portion of the sensor data that is indicative of a set of movements ofthe electronic device in association with the vehicle; detecting aninstance of the electronic device connecting to the vehicle based uponthe sensor data; and identifying a portion of the acceleration data thatis in temporal proximity to the instance of the electronic deviceconnecting to the vehicle.
 20. The computer-implemented method of claim17, further comprising: determining a portion of the sensor data that isindicative of a set of movements of the electronic device in associationwith the vehicle; and determining the portion of the sensor data that isindicative of movements of the vehicle based upon the location data andthe acceleration data.